Methodological challenges in pragmatic trials in Alzheimer’s disease and related dementias: Opportunities for improvement

Background and Aims We need more pragmatic trials of interventions to improve care and outcomes for people living with Alzheimer’s disease and related dementias. However, these trials present unique methodological challenges in their design, analysis, and reporting—often, due to the presence of one or more sources of clustering. Failure to account for clustering in the design and analysis can lead to increased risks of Type I and Type II errors. We conducted a review to describe key methodological characteristics and obtain a “baseline assessment” of methodological quality of pragmatic trials in dementia research, with a view to developing new methods and practical guidance to support investigators and methodologists conducting pragmatic trials in this field. Methods We used a published search filter in MEDLINE to identify trials more likely to be pragmatic and identified a subset that focused on people living with Alzheimer’s disease or other dementias or included them as a defined subgroup. Pairs of reviewers extracted descriptive information and key methodological quality indicators from each trial. Results We identified N = 62 eligible primary trial reports published across 36 different journals. There were 15 (24%) individually randomized, 38 (61%) cluster randomized, and 9 (15%) individually randomized group treatment designs; 54 (87%) trials used repeated measures on the same individual and/or cluster over time and 17 (27%) had a multivariate primary outcome (e.g. due to measuring an outcome on both the patient and their caregiver). Of the 38 cluster randomized trials, 16 (42%) did not report sample size calculations accounting for the intracluster correlation and 13 (34%) did not account for intracluster correlation in the analysis. Of the 9 individually randomized group treatment trials, 6 (67%) did not report sample size calculations accounting for intracluster correlation and 8 (89%) did not account for it in the analysis. Of the 54 trials with repeated measurements, 45 (83%) did not report sample size calculations accounting for repeated measurements and 19 (35%) did not utilize at least some of the repeated measures in the analysis. No trials accounted for the multivariate nature of their primary outcomes in sample size calculation; only one did so in the analysis. Conclusion There is a need and opportunity to improve the design, analysis, and reporting of pragmatic trials in dementia research. Investigators should pay attention to the potential presence of one or more sources of clustering. While methods for longitudinal and cluster randomized trials are well developed, accessible resources and new methods for dealing with multiple sources of clustering are required. Involvement of a statistician with expertise in longitudinal and clustered designs is recommended.

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Intracluster correlation coefficients in the Greater Mekong Subregion for sample size calculations of cluster randomized malaria trials

Malaria Journal ◽

10.1186/s12936-019-3062-x ◽

2019 ◽

Vol 18 (1) ◽

Cited By ~ 1

Author(s):

Pimnara Peerawaranun ◽

Jordi Landier ◽

Francois H. Nosten ◽

Thuy-Nhien Nguyen ◽

Tran Tinh Hien ◽

...

Keyword(s):

Randomized Trial ◽

Sample Size ◽

Randomized Trials ◽

Cluster Randomized Trial ◽

Cluster Randomized Trials ◽

Intracluster Correlation ◽

Variable Approach ◽

Sample Size Calculations ◽

Cluster Randomized ◽

Greater Mekong Subregion

Abstract Background Sample size calculations for cluster randomized trials are a recognized methodological challenge for malaria research in pre-elimination settings. Positively correlated responses from the participants in the same cluster are a key feature in the estimated sample size required for a cluster randomized trial. The degree of correlation is measured by the intracluster correlation coefficient (ICC) where a higher coefficient suggests a closer correlation hence less heterogeneity within clusters but more heterogeneity between clusters. Methods Data on uPCR-detected Plasmodium falciparum and Plasmodium vivax infections from a recent cluster randomized trial which aimed at interrupting malaria transmission through mass drug administrations were used to calculate the ICCs for prevalence and incidence of Plasmodium infections. The trial was conducted in four countries in the Greater Mekong Subregion, Laos, Myanmar, Vietnam and Cambodia. Exact and simulation approaches were used to estimate ICC values for both the prevalence and the incidence of parasitaemia. In addition, the latent variable approach to estimate ICCs for the prevalence was utilized. Results The ICCs for prevalence ranged between 0.001 and 0.082 for all countries. The ICC from the combined 16 villages in the Greater Mekong Subregion were 0.26 and 0.21 for P. falciparum and P. vivax respectively. The ICCs for incidence of parasitaemia ranged between 0.002 and 0.075 for Myanmar, Cambodia and Vietnam. There were very high ICCs for incidence in the range of 0.701 to 0.806 in Laos during follow-up. Conclusion ICC estimates can help researchers when designing malaria cluster randomized trials. A high variability in ICCs and hence sample size requirements between study sites was observed. Realistic sample size estimates for cluster randomized malaria trials in the Greater Mekong Subregion have to assume high between cluster heterogeneity and ICCs. This work focused on uPCR-detected infections; there remains a need to develop more ICC references for trials designed around prevalence and incidence of clinical outcomes. Adequately powered trials are critical to estimate the benefit of interventions to malaria in a reliable and reproducible fashion. Trial registration: ClinicalTrials.govNCT01872702. Registered 7 June 2013. Retrospectively registered. https://clinicaltrials.gov/ct2/show/NCT01872702

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Sample size determination for stepped wedge cluster randomized trials in pragmatic settings

Statistical Methods in Medical Research ◽

10.1177/09622802211022392 ◽

2021 ◽

pp. 096228022110223

Author(s):

Jijia Wang ◽

Jing Cao ◽

Song Zhang ◽

Chul Ahn

Keyword(s):

Sample Size ◽

Closed Form ◽

Sample Size Calculation ◽

Repeated Measurements ◽

Sample Size Determination ◽

Stepped Wedge ◽

Pragmatic Trials ◽

Stepped Wedge Trials ◽

Cluster Randomized ◽

The Impact

The stepped-wedge cluster randomized design has been increasingly employed by pragmatic trials in health services research. In this study, based on the GEE approach, we present closed-form sample size calculation that is applicable to both closed-cohort and cross-sectional stepped wedge trials. Importantly, the proposed method is flexible to accommodate design issues routinely encountered in pragmatic trials, such as different within- and between-subject correlation structures, irregular crossover schedules for the switch to intervention, and missing data due to repeated measurements over prolonged follow-up. The closed-form formulas allow researchers to analytically assess the impact of different design factors on sample size requirement. We also recognize the potential issue of limited numbers of clusters in pragmatic stepped wedge trials and present an adjustment approach for underestimated variance of the treatment effect. We conduct extensive simulation to assess the performance of the proposed sample size method. An application example to a real clinical trial is presented.

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A tutorial on sample size calculation for multiple-period cluster randomized parallel, cross-over and stepped-wedge trials using the Shiny CRT Calculator

International Journal of Epidemiology ◽

10.1093/ije/dyz237 ◽

2020 ◽

Vol 49 (3) ◽

pp. 979-995 ◽

Cited By ~ 4

Author(s):

Karla Hemming ◽

Jessica Kasza ◽

Richard Hooper ◽

Andrew Forbes ◽

Monica Taljaard

Keyword(s):

Sample Size ◽

Group Treatment ◽

Single Point ◽

Sample Size Calculation ◽

Stepped Wedge ◽

Cross Sectional ◽

Multiple Observations ◽

Sample Size Calculations ◽

Correlation Structures ◽

Cluster Randomized

Abstract It has long been recognized that sample size calculations for cluster randomized trials require consideration of the correlation between multiple observations within the same cluster. When measurements are taken at anything other than a single point in time, these correlations depend not only on the cluster but also on the time separation between measurements and additionally, on whether different participants (cross-sectional designs) or the same participants (cohort designs) are repeatedly measured. This is particularly relevant in trials with multiple periods of measurement, such as the cluster cross-over and stepped-wedge designs, but also to some degree in parallel designs. Several papers describing sample size methodology for these designs have been published, but this methodology might not be accessible to all researchers. In this article we provide a tutorial on sample size calculation for cluster randomized designs with particular emphasis on designs with multiple periods of measurement and provide a web-based tool, the Shiny CRT Calculator, to allow researchers to easily conduct these sample size calculations. We consider both cross-sectional and cohort designs and allow for a variety of assumed within-cluster correlation structures. We consider cluster heterogeneity in treatment effects (for designs where treatment is crossed with cluster), as well as individually randomized group-treatment trials with differential clustering between arms, for example designs where clustering arises from interventions being delivered in groups. The calculator will compute power or precision, as a function of cluster size or number of clusters, for a wide variety of designs and correlation structures. We illustrate the methodology and the flexibility of the Shiny CRT Calculator using a range of examples.

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Differential diagnosis of Alzheimer’s disease using spectrochemical analysis of blood

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.1701517114 ◽

2017 ◽

Vol 114 (38) ◽

pp. E7929-E7938 ◽

Cited By ~ 56

Author(s):

Maria Paraskevaidi ◽

Camilo L. M. Morais ◽

Kássio M. G. Lima ◽

Julie S. Snowden ◽

Jennifer A. Saxon ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Neurodegenerative Diseases ◽

Sensitivity And Specificity ◽

Lewy Bodies ◽

Total Reflection ◽

Attenuated Total Reflection ◽

Repeated Measurements ◽

Healthy Individuals ◽

Different Types

The progressive aging of the world’s population makes a higher prevalence of neurodegenerative diseases inevitable. The necessity for an accurate, but at the same time, inexpensive and minimally invasive, diagnostic test is urgently required, not only to confirm the presence of the disease but also to discriminate between different types of dementia to provide the appropriate management and treatment. In this study, attenuated total reflection FTIR (ATR-FTIR) spectroscopy combined with chemometric techniques were used to analyze blood plasma samples from our cohort. Blood samples are easily collected by conventional venepuncture, permitting repeated measurements from the same individuals to monitor their progression throughout the years or evaluate any tested drugs. We included 549 individuals: 347 with various neurodegenerative diseases and 202 age-matched healthy individuals. Alzheimer’s disease (AD;n= 164) was identified with 70% sensitivity and specificity, which after the incorporation of apolipoprotein ε4 genotype (APOEε4) information, increased to 86% when individuals carried one or two alleles of ε4, and to 72% sensitivity and 77% specificity when individuals did not carry ε4 alleles. Early AD cases (n= 14) were identified with 80% sensitivity and 74% specificity. Segregation of AD from dementia with Lewy bodies (DLB;n= 34) was achieved with 90% sensitivity and specificity. Other neurodegenerative diseases, such as frontotemporal dementia (FTD;n= 30), Parkinson’s disease (PD;n= 32), and progressive supranuclear palsy (PSP;n= 31), were included in our cohort for diagnostic purposes. Our method allows for both rapid and robust diagnosis of neurodegeneration and segregation between different dementias.

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The power of sample size through a multi-scanner approach in MR neuroimaging regression analysis: evidence from Alzheimer’s disease with and without depression

Australasian Physical & Engineering Sciences in Medicine ◽

10.1007/s13246-019-00758-1 ◽

2019 ◽

Vol 42 (2) ◽

pp. 563-571 ◽

Cited By ~ 1

Author(s):

Efstratios Karavasilis ◽

Theodore P. Parthimos ◽

John D. Papatriantafyllou ◽

Foteini Christidi ◽

Sokratis G. Papageorgiou ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Regression Analysis ◽

Sample Size

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Sample size calculations for repeated measures experiments

Controlled Clinical Trials ◽

10.1016/s0197-2456(95)00086-0 ◽

1995 ◽

Vol 16 (6) ◽

pp. 449-450 ◽

Cited By ~ 2

Author(s):

James Rochon

Keyword(s):

Sample Size ◽

Repeated Measures ◽

Sample Size Calculations

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A new dependence parameter approach to improve the design of cluster randomized trials with binary outcomes

Clinical Trials ◽

10.1177/1740774511423851 ◽

2011 ◽

Vol 8 (6) ◽

pp. 687-698 ◽

Cited By ~ 15

Author(s):

Catherine M Crespi ◽

Weng Kee Wong ◽

Sheng Wu

Keyword(s):

Sample Size ◽

Randomized Trials ◽

Pearson Correlation ◽

Sample Size Determination ◽

Binary Outcomes ◽

Alternative Measure ◽

Cluster Randomized Trials ◽

Sample Size Calculations ◽

Cluster Randomized ◽

Measure Of Dependence

Background and Purpose Power and sample size calculations for cluster randomized trials require prediction of the degree of correlation that will be realized among outcomes of participants in the same cluster. This correlation is typically quantified as the intraclass correlation coefficient (ICC), defined as the Pearson correlation between two members of the same cluster or proportion of the total variance attributable to variance between clusters. It is widely known but perhaps not fully appreciated that for binary outcomes, the ICC is a function of outcome prevalence. Hence, the ICC and the outcome prevalence are intrinsically related, making the ICC poorly generalizable across study conditions and between studies with different outcome prevalences. Methods We use a simple parametrization of the ICC that aims to isolate that part of the ICC that measures dependence among responses within a cluster from the outcome prevalence. We incorporate this parametrization into sample size calculations for cluster randomized trials and compare our method to the traditional approach using the ICC. Results Our dependence parameter, R, may be less influenced by outcome prevalence and has an intuitive meaning that facilitates interpretation. Estimates of R from previous studies can be obtained using simple statistics. Comparison of methods showed that the traditional ICC approach to sample size determination tends to overpower studies under many scenarios, calling for more clusters than truly required. Limitations The methods are developed for equal-sized clusters, whereas cluster size may vary in practice. Conclusions The dependence parameter R is an alternative measure of dependence among binary outcomes in cluster randomized trials that has a number of advantages over the ICC.

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